Resource allocation and computation offloading with data security for mobile edge computing

Elgendy, Ibrahim A.; Zhang, Weizhe; Tian, Yu-Chu; Li, Keqin

doi:10.1016/j.future.2019.05.037

Cited by 121 publications

(89 citation statements)

References 31 publications

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“…MEC has emerged as a key technology to overcome the problems related to limited battery capacity and computational resources of the mobile devices [11]- [13]. In [29], the authors explained the role of MEC and identified energy consumption and latency as the prime performance indicators for task offloading.…”

Section: A Energy-efficient and Latency-critical Mecmentioning

confidence: 99%

“…To confront these technical requirements, mobile edge computing (MEC) has been introduced as an efficient solution for task offloading [11]- [13]. MEC achieves low transmission delay compared to cloud computing by reducing the distance between the servers and end users [11].…”

Section: Introductionmentioning

confidence: 99%

“…Privacy and security concerns of the offloaded tasks are major threats in MEC [13], [23]. The MEC servers, which are located at the network edge, are in close proximity to the attackers and vulnerable to hostile attacks [24].…”

Section: Introductionmentioning

confidence: 99%

“…• We also analyze the system performance of our introduced technique and compare them with existing solutions [4], [5], [13], [28]. The system performance is measured in terms of energy consumption, probable security-breach cost, and average delay.…”

Section: Introductionmentioning

confidence: 99%

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Green and Secure Computation Offloading for Cache-Enabled IoT Networks

et al. 2020

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The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies. INDEX TERMS Caching, energy, IoT, mobile edge computing, security. DARYOUSH HABIBI (Senior Member, IEEE) received the B.E. degree (Hons.

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Section: A Energy-efficient and Latency-critical Mecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Green and Secure Computation Offloading for Cache-Enabled IoT Networks

et al. 2020

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“…Many researchers have proposed different kinds of offloading frameworks (Mazouzi et al, 2019;Zhang et al, 2019;Elgendy et al, 2019) While some offload the entire application, others select only heavy computational modules. The number of factors that needs to be considered for offloading the modules is network bandwidth, transmission cost, computational cost, available memory and latency.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

Veeraiyan¹,

Kousika²,

Senthilkumar³

et al. 2020

Journal of Computer Science

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Offloading the execution of heavy computational modules from mobile devices to Mobile Cloud Computing (MCC) is inevitable in today's era as it mainly focuses in consuming less battery power and execution time. But, the problem incurred with identifying the most optimal cloud device to map each module still remains a challenge in cloud computing environment. In this paper, a novel MCC offloading framework is proposed to fasten the allocation and execution of high computational modules that runs in the mobile device, effectively on the cloud. The framework employs K-Means clustering algorithm to group the nearest cloud virtual machines that best suits for executing modules of software running in the mobile. The objective of the paper is to maximize the energy savings by extending the battery life and execution speed of mobile device when executing heavy computational modules. The optimal selection of cloud device is attained by grouping the requirements of each module with the nearest cloud devices offering the same requirements using K-Means Algorithm. The proposed framework is compared with the existing mobile computation offloading frameworks with respect to energy saving, execution time and energy consumption. The results show that the proposed work executes the modules of computationally intensive modules in minimum time span with maximized energy savings than the existing frameworks.

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HRF‐ExGB: Hybrid random forest‐extreme gradient boosting for mobile edge computing

Anuradha,

Jean Justus,

Vijayalakshmi

et al. 2024

Trans Emerging Tel Tech

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The development of increasingly cutting‐edge mobile apps like augmented reality, facial recognition, and natural language processing has been facilitated by the sharp rise in smartphone demand. The increased use of mobile devices like wireless sensors and wearable technology has led to a rapid increase in mobile applications. Due to the explosive growth of the Internet and distributed computing resources of edge devices in mobile edge computing (MEC), it is necessary to have a suitable controller to ensure effective utilization of distributed computing resources. However, the existing approaches can lead to more computation time, more consumption of energy, and a lack of security issues. To overcome these issues, this paper proposed a novel approach called Hybrid Random Forest‐Extreme Gradient Boosting (HRF‐XGBoost) to enhance the computation offloading and joint resource allocation predictions. In a wireless‐powered multiuser MEC system, the starling murmuration optimization model is utilized to figure out the ideal task offloading options. XGBoost is combined with a random forest classifier to form an HRF‐XGBoost architecture which is used to speed up the process while preserving the user's device's battery. An offloading method is created employing certain processes once the best computation offloading decision for Mobile Users (MUs) has been established. The experiment result shows that the method reduced system overhead and time complexity using the strategy of selecting fewer tasks alone by optimally eliminating other tasks. It optimizes the execution time even when the mobile user increases. The performance of the overall system can be greatly improved by our model compared to other existing techniques.

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Resource allocation and computation offloading with data security for mobile edge computing

Cited by 121 publications

References 31 publications

Green and Secure Computation Offloading for Cache-Enabled IoT Networks

Green and Secure Computation Offloading for Cache-Enabled IoT Networks

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

HRF‐ExGB: Hybrid random forest‐extreme gradient boosting for mobile edge computing

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